Graph Engineering

Graph engineering is the discipline of designing the structures agents work through — not the prompts. It has two halves:

1. Knowledge graphs — what agents remember. Nodes are entities and facts, edges are relationships with time and provenance. This file's 9-stage pipeline covers it, distilled from Southeast University's graduate KG course (https://github.com/npubird/KnowledgeGraphCourse, Prof. Peng Wang), translated to English and adapted for LLM-era agents.
2. Task graphs — how agents work. Nodes are jobs, edges are execution dependencies: parallel fan-out, separate verifier contexts, the stop rule, the human gate. Read references/task-graphs.md when the request is about orchestrating agents rather than building memory. The task graph decides the execution shape once; use teammode only for a ready parallel component whose members must coordinate. Keep serial chains with one agent and perfectly isolated parallel nodes as plain subagents.